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import numpy as np
import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
class PatchFeatureExtractor(nn.Module):
x_mean = torch.FloatTensor(np.array([0.485, 0.456, 0.406])[None, :, None, None])
x_std = torch.FloatTensor(np.array([0.229, 0.224, 0.225])[None, :, None, None])
def __init__(self, patch_num=256, input_shape=None):
super(PatchFeatureExtractor, self).__init__()
if input_shape is None:
input_shape = [3, 512, 1024]
self.patch_dim = 1024
self.patch_num = patch_num
img_channel = input_shape[0]
img_h = input_shape[1]
img_w = input_shape[2]
p_h, p_w = img_h, img_w // self.patch_num
p_dim = p_h * p_w * img_channel
self.patch_embedding = nn.Sequential(
Rearrange('b c h (p_n p_w) -> b p_n (h p_w c)', p_w=p_w),
nn.Linear(p_dim, self.patch_dim)
)
self.x_mean.requires_grad = False
self.x_std.requires_grad = False
def _prepare_x(self, x):
x = x.clone()
if self.x_mean.device != x.device:
self.x_mean = self.x_mean.to(x.device)
self.x_std = self.x_std.to(x.device)
x[:, :3] = (x[:, :3] - self.x_mean) / self.x_std
return x
def forward(self, x):
# x [b 3 512 1024]
x = self._prepare_x(x) # [b 3 512 1024]
x = self.patch_embedding(x) # [b 256(patch_num) 1024(d)]
x = x.permute(0, 2, 1) # [b 1024(d) 256(patch_num)]
return x
if __name__ == '__main__':
from PIL import Image
extractor = PatchFeatureExtractor()
img = np.array(Image.open("../../src/demo.png")).transpose((2, 0, 1))
input = torch.Tensor([img]) # 1 3 512 1024
feature = extractor(input)
print(feature.shape) # 1, 1024, 256
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